Agricultural Product Price and Crop Prediction Based on Data Science Technique

Agriculture serves as a crucial catalyst for economic growth and forms the foundation of developing countries' economies. Farmers are suffering losses in their agricultural operations due to a lack of appropriate industry expertise. Machine learning is a crucial approach for estimating the cost and productivity of farming. In relation to this specific situation, we have devised a model that will aid farmers in choosing the optimal crop to cultivate throughout each season and evaluating the associated expenses. By employing cost forecasting, farmers can mitigate the risk of suffering losses. We employed the supervised machine learning approach (SMLT) to analyze the crop dataset. To ascertain the algorithm that achieves the highest level of accuracy, we conducted a comparison analysis of the different algorithms. This paper will generate a forecast that will help farmers determine the most fruitful crop to cultivate. The forecast will consider multiple parameters, such as temperature, humidity, geographical location, land area, and rainfall.